Yuqi Fang, Wei Wang, Qianqian Wang, Hong-Jun Li, Mingxia Liu
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引用次数: 0
Abstract
Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools. However, they mainly utilize a single imaging modality, neglecting complementary information provided by structural and functional MRI. To this end, we propose an attention-enhanced structural and functional MRI fusion (ASFF) framework for HIV-associated ANI analysis. Specifically, the ASFF first extracts data-driven and human-engineered features from structural MRI, and also captures functional MRI features via a graph isomorphism network and Transformer. A mutual cross-attention fusion module is then designed to model the underlying relationship between structural and functional MRI. Additionally, a semantic inter-modality constraint is introduced to encourage consistency of multimodal features, facilitating effective feature fusion. Experimental results on 137 subjects from an HIV-associated ANI dataset with T1-weighted MRI and resting-state functional MRI show the effectiveness of our ASFF in ANI identification. Furthermore, our method can identify both modality-shared and modality-specific brain regions, which may advance our understanding of the structural and functional pathology underlying ANI.
无症状神经认知功能障碍(ANI)是人类免疫缺陷病毒(HIV)感染者认知功能障碍的主要表现形式。目前 ANI 的诊断标准主要依赖于主观临床评估,这可能会导致临床医生之间产生不同的解释。最近的一些研究利用含有客观生物标志物的结构性或功能性磁共振成像进行 ANI 分析,为临床医生提供了辅助诊断工具。然而,这些研究主要利用单一成像模式,忽略了结构性和功能性 MRI 提供的互补信息。为此,我们提出了一种用于艾滋病相关 ANI 分析的注意力增强结构和功能 MRI 融合(ASFF)框架。具体来说,ASFF 首先从结构磁共振成像中提取数据驱动和人为设计的特征,然后通过图同构网络和 Transformer 捕捉功能磁共振成像特征。然后设计一个相互交叉关注融合模块,以模拟结构性和功能性 MRI 之间的潜在关系。此外,还引入了语义跨模态约束,以鼓励多模态特征的一致性,从而促进有效的特征融合。实验结果显示,我们的 ASFF 在 ANI 识别方面非常有效。此外,我们的方法还能识别模式共享和模式特异的脑区,这可能会促进我们对 ANI 的结构和功能病理的理解。